Logistics Regression using Pytorch

LogisticsRegression.py

import torch 
import numpy as np
import torch.nn as nn
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split

bc =  datasets.load_breast_cancer()
x, y = bc.data, bc.target

n_samples, n_features = x.shape
print(n_samples, n_features)

X_train,X_test,y_train,y_test = train_test_split(x,y,test_size=.2,random_state=42)
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

X_train = torch.from_numpy(X_train.astype(np.float32))
X_test = torch.from_numpy(X_test.astype(np.float32))
y_train = torch.from_numpy(y_train.astype(np.float32))
y_test = torch.from_numpy(y_test.astype(np.float32))
y_train = y_train.view(y_train.shape[0],1)
y_test = y_test.view(y_test.shape[0],1)

class LogisticsRegression(nn.Module):
    def __init__(self,input_dim):
        super(LogisticsRegression,self).__init__()
        self.linear = nn.Linear(input_dim,1)
    def forward(self,x):
        return torch.sigmoid(self.linear(x))

model = LogisticsRegression(n_features)
learning_rate = .01
criterion = nn.BCELoss()
optmizer = torch.optim.Adam(model.parameters(),lr=learning_rate)
n_iter = 100

for epoch in range(n_iter):
    y_pred = model(X_train)
    loss = criterion(y_pred,y_train)
    loss.backward()
    optmizer.step()
    optmizer.zero_grad()
    if (epoch+1)%10 ==0:
        print(f"epoch: {epoch + 1}, loss={loss:.4f}")
        
        
with torch.no_grad():
    y_predicted = model(X_test)
    y_predicted_class = y_predicted.round()
    acc = (y_predicted_class.eq(y_test).sum() / float(y_predicted.shape[0])) * 100
    print(f"Accuracy: {acc:.4f}")